The detection of targets and/or anomalies in images having nonuniform backgrounds has numerous practical applications. For example, sensors may be employed in observational astronomy for the detection and tracking of stars, planets, and other extraterrestrial objects. In addition, target detection is also important in military applications, including, for example, the long range electro-optical detection, tracking, and discrimination of targets and decoys. Similarly, target detection may be used in the imaging and analysis of cells and particles for medical applications.
Although desirable results have been achieved using prior art target detection systems, there is room for improvement. For example, prior art detection techniques such as moving target detection and shape-based detection may be less than optimally-effective in detecting partially concealed targets. Other prior art hyperspectral (HS) detection techniques may be better suited for such an application, however, they are typically handicapped by algorithms which apply a scene-wide spectral-matched filter to a HS scene in order to detect targets. The matched filter can be optimized to look for the target while rejecting scene-wide background materials, but unfortunately, many HS scenes do not have uniform backgrounds, and are instead comprised of multiple regions having varying backgrounds. In such cases, prior art HS detection techniques may lead to sub-optimal performance since the single existing background rejection filter is constructed using generalized scene-wide information, which may be inappropriate for localized background areas in which a target may be located. Such application of a generalized filter to local areas within the image for which the filter is not suited may not only significantly decrease the probability of target detection, but might also increase the probability of registering false alarms as well. As a result, existing HS detection techniques can often be rendered inadequate for reconnaissance, surveillance, and tracking scenarios which require the detection of changes in imagery in real time, or with respect to previously acquired imagery. Therefore, there is a continuing impetus to increase the accuracy and precision of target detection techniques.
In addition, HS data volumes tend to be very large since they incorporate hundreds of spectral channels, requiring data compression for transmission and storage in many cases. For example, a single scene can contain over 200 MB of data, making uncompressed data transmission and storage very slow and cumbersome. Prior art principal component analysis techniques can be used to compress these data volumes, but the resulting representations can be low fidelity due to unaccounted-for spectral variation within a scene. As a result, existing HS detection techniques can often be rendered inadequate for reconnaissance, surveillance, and tracking scenarios, which require the transmission of HS imagery in real time. Therefore, there is a continuing impetus to decrease the data volumes associated with HS target detection techniques.